Progressive Multi-Fidelity Learning

Last updated on Oct 17, 2025
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We propose a progressive multi-fidelity surrogate modeling paradigm, designed to incorporate new datasets of varying modalities as they become available. This allows to progressively enhance prediction accuracy and reduce uncertainty, while effectively ensuring knowledge retention across updates.

Contributors: Paolo Conti.

Deep Learning
Paolo Conti
Paolo Conti
Research Associate

My research interests include scientific machine learning, multi-fidelity and reduced-order modeling.

Publications

Progressive multi-fidelity learning for physical system predictions
Highly accurate datasets from numerical or physical experiments are often expensive and time-consuming to acquire, posing a significant …
Paolo Conti, Mengwu Guo, Attilio Frangi, Andrea Manzoni
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Progressive multi-fidelity learning for physical system predictions